Treatment of diabetic kidney disease. A network meta-analysis

Fabian Büttner; Clara Vollmer Barbosa; Hannah Lang; Zhejia Tian; Anette Melk; Bernhard M W Schmidt

doi:10.1371/journal.pone.0293183

. 2023 Nov 2;18(11):e0293183. doi: 10.1371/journal.pone.0293183

Treatment of diabetic kidney disease. A network meta-analysis

Fabian Büttner ^1,^#, Clara Vollmer Barbosa ^1,^#, Hannah Lang ¹, Zhejia Tian ¹, Anette Melk ^2,^‡, Bernhard M W Schmidt ^1,^‡,^*

Editor: Licy Yanes Cardozo³

PMCID: PMC10621862 PMID: 37917640

Abstract

Background

Diabetic kidney disease (DKD) is a health burden of rising importance. Slowing progression to end stage kidney disease is the main goal of drug treatment. The aim of this analysis is to compare drug treatments of DKD by means of a systemic review and a network meta-analysis.

Methods

We searched Medline, CENTRAL and clinicaltrials.gov for randomized, controlled studies including adults with DKD treated with the following drugs of interest: single angiotensin-converting-enzyme-inhibitor or angiotensin-receptor-blocker (single ACEi/ARB), angiotensin-converting-enzyme-inhibitor and angiotensin-receptor-blocker combination (ACEi+ARB combination), aldosterone antagonists, direct renin inhibitors, non-steroidal mineralocorticoid-receptor-antagonists (nsMRA) and sodium-glucose cotransporter-2 inhibitors (SGLT2i). As primary endpoints, we defined: overall mortality and end-stage kidney disease, as secondary endpoints: renal composite outcome and albuminuria and as safety endpoints: acute kidney injury, hyperkalemia and hypotension. Under the use of a random effects model, we computed the overall effect estimates using the statistic program R4.1 and the corresponding package “netmeta”. Risk of bias was assessed using the RoB 2 tool and the quality of evidence of each pairwise comparison was rated according to GRADE (Grading of Recommendations Assessment, Development and Evaluation).

Results

Of initial 3489 publications, 38 clinical trials were found eligible, in total including 42346 patients. Concerning the primary endpoints overall mortality and end stage kidney disease, SGLT2i on top of single ACEi/ARB compared to single ACEi/ARB was the only intervention significantly reducing the odds of mortality (OR 0.81, 95%CI 0.70–0.95) and end-stage kidney disease (OR 0.69, 95%CI 0.54–0.88). The indirect comparison of nsMRA vs SGLT2i in our composite endpoint suggests a superiority of SGLT2i (OR 0.60, 95%CI 0.47–0.76). Concerning safety endpoints, nsMRA and SGLT2i showed benefits compared to the others.

Conclusions

As the only drug class, SGLT2i showed in our analysis beneficial effects on top of ACEi/ARB treatment regarding mortality and end stage kidney disease and by that reconfirmed its position as treatment option for diabetic kidney disease. nsMRA reduced the odds for a combined renal endpoint and did not raise any safety concerns, justifying its application.

Introduction

Diabetes mellitus (DM) ranks among the top ten most devastating diseases worldwide, contributing to enormous disability and mortality [1]. The major burden of DM is primarily driven by its complications, among which diabetic kidney disease (DKD) is one of the most harmful. The incidence of DKD has globally substantially increased over the past three decades [2], which lead to the inevitable necessity to develop novel effective drugs to preserve kidney function, decrease the cardiovascular risk or even prevent its onset.

The treatment of DKD is based on renin-angiotensin-aldosterone system (RAAS) blockade since the 90´s. RAAS is a hormonal system that regulates the fluid and electrolyte balance and the systemic vascular system in the human body through various pathways, that affect the kidney. Drugs based on a single RAAS Blockade in particular angiotensin-converting enzyme inhibitor (ACEi) or an angiotensin receptor blocker (ARB), were the only pharmacological approach to show considerable advantages [3]. The evidence for the benefit of an additional RAAS blockade agent, either by combining ACEi+ ARB or one of them with e.g. mineralocorticoid receptor antagonists (MRA) or a direct renin inhibitor (DRI), remained limited [3]. In addition, safety concerns regarding development of hyperkalemia or acute kidney injury (abrupt changes in kidney function, including changes in serum creatinine and urine output within 48 hours or 7 days) [4] prohibited widespread use of these treatments [5].

With the emergence of a new generation of drugs (e.g. sodium-glucose cotransporter-2 inhibitors (SGLT2i), non-steroidal mineralocorticoid receptor antagonists (nsMRAs)) ground breaking impulses were given to the therapy of DKD, supported by studies showing a reduced incidence of combined renal endpoints with acceptable safety profiles [6]. SGLT2i block the carrier protein sodium-glucose linked transporter 2, which induces glucosuria and by that initiating a cascade positively affecting the cardiovascular system and kidney. nsMRA (e.g. finerenone, esaxerenone) bind highly selective on mineralocorticoid receptors and by that antagonizes the effect of aldosterone, which is steroid hormone and assumed to play a pivotal role in the development of chronic kidney disease. But, due to the lack of direct comparisons, it is still a matter of debate, which interventions show the greatest benefit in patients with DKD.

Our objective is to identify the optimal treatment strategy in addition to ACEi or ARB for patients with DKD. We mainly focused on the following questions:

Which drug most effectively reduces the risk of overall mortality, the development of end stage kidney disease, acute kidney injury, hyperkalemia and hypotension?
Which medication carries the least risk of inducing the specific side effects we have outlined?

To facilitate this goal, we conducted a systemic review and network meta-analysis enabling direct and indirect estimates of comparative efficacy for different interventions, specifically addressing overall mortality, incidence of end stage kidney disease (ESKD), change of albuminuria, incidence of acute kidney injury, hyperkalemia and hypotension. Thus, providing an overview of the current state of the art in the treatment of DKD and offering evidence-based guidance.

Methods

The protocol for this analysis was registered with Prospero (CRD42021238011) and reported according to the PRISMA statements 2020 (S3 File).

We searched Cochrane Central Register of Controlled Trials (CENTRAL), Medline and clinicaltrials.gov by the following search terms: (diabetic nephropathy OR (diabetes AND kidney) OR chronic kidney disease OR CKD) AND (RCT OR randomized) combined with names of the investigated drugs (S4 File). We included only prospective, randomized, controlled clinical trials published in English from 1995 till May 2022 on with a minimum duration of 8 weeks. The deliberate choice of 8 weeks minimum allowed the inclusion of studies with specific safety endpoints (e.g., hyperkalemia). We excluded cross-over trials and retracted publications. Control groups met our inclusion criteria if they consisted of an intervention included in our analysis or a placebo on the background treatment with single ACEi/ARB.

Interventions of interest were single ACEi/ARB, ACEi+ARB combination, MRAs, DRI, nsMRAs (finerenone, esaxerenone) and SGLT2i. Due to the lack of studies fulfilling our eligibility criteria, several drug regimens (neprilysin inhibitors, high dose RAAS blockade) could not be considered. If a minimum of 80% of patients in a study were taking either ACEi or ARB, our criteria “background treatment with single ACEi/ARB” was fulfilled. Besides single ACEi/ARB, all included interventions were required to be on top of the background treatment (single ACEi or ARB treatment). All study participants were required to have diabetes mellitus and chronic kidney disease (CKD, as defined by the KDIGO criteria). Exclusion criteria were patients having other kidney diseases (e.g. glomerulonephritis), or being already on renal replacement therapy and pediatric studies.

We decided to use single ACEi/ARB as reference intervention for all comparisons, because it is the standard care for diabetic patients with CKD (KDIGO guideline 2022).

Three reviewers (FB, CV, BS) were involved in the study selection process using a pre-defined set of criteria through three levels of screening: title, abstract and full text. The screening at the first and second level (title & abstract) was performed by two independent authors (FB, CV) and checked by the third author (BS). At the final level ("full text") all publications were completely assessed according to our eligibility criteria and selected through consent by all reviewers (CV, BS, ZT, HL, FB, AM). No trial was excluded due to insufficient information at the abstract level. We considered as insufficient information, if a study misses for our analysis relevant outcome data, did not state if it only included patient with DKD or did not completely describe the study design (e.g. randomized, controlled). In these cases, final decisions were made at the full text level (Fig 1). Conflicts over study inclusion and data extraction were resolved by discussion with the third author (BS).

Data extraction was then performed by the two reviewers (FB, CV), subsequently the members of the team (ZT, HL, AM, BS) checked and eventually discussed the extracted data.

In case of missing data (e.g. authors did not state the background therapy or did not give enough details about study participants) study investigators were contacted via e-mail. For relevant outcome data presented only on form of figures without numerical values, we applied an image extraction software (digitizeit 2.5.; I. Bormann, Braunschweig, Germany). The following characteristics were extracted study name, registration number, background therapy, type of study, inclusion criteria, exclusion criteria, study duration, study drug, dosage, mean age, percentage of male patients, outcome data and history of hypertension.

Outcomes

Our primary outcomes were overall mortality and end stage kidney disease (ESKD). Secondary outcomes were albuminuria and a renal composite outcome (sustained eGFR of less than 15 mL/ min/1.73 m2, a sustained eGFR decline of 40% from baseline, initiation of kidney replacement therapy or kidney death). eGFR (Estimated Glomerular Filtration Rate) is a clinical measurement used in nephrology to estimate the rate at which the glomeruli are filtering waste products and excess substances from the blood per unit of time. Safety outcomes were acute kidney injury, hyperkalemia and hypotension. Binary effect measures were estimated as odds ratio (OR) and continuous as standardized mean difference (SMD), all effect estimates are reported with 95% confidence interval (95% CI). We excluded post-hoc renal death as endpoint, due to the lack of its report.

Data analysis

Data analysis was performed with the statistic program R version 4.0.4 [7] using the “netmeta” package. Under the assumption of transitivity, we conducted a network meta-analysis using the frequentist model. A network meta-analysis is a statistical method for directly or indirectly comparing multiple interventions. It allows us to calculate the comparative effectiveness of interventions that have not been studied in head-to-head trials or have not been adequately studied. For each intervention, we applied the random effect model to generate the study effect sizes. The specific R-code and data sheet for our network meta-analysis can be found in the S15 and S16 Files.

Risk of bias was assessed by two authors using the RoB 2 tool (The Cochrane Collaboration’s tool for assessing risk of bias) and additionally reporting bias through comparison adjusted funnel plots which was applied to compare older treatments with newer treatments [8, 9].

With the use of R (“netmeta” package) and Hedges´g method we computed the standardized mean difference. We computed the network plot with the “netgraph” function from the “netmeta” package, the comparison-adjusted funnel plots with the function “funnel” and the splitting of direct and indirect evidence with the “netsplit” function.

We assumed a uniformly distributed heterogeneity across the comparisons and used the I² test for the overall heterogeneity assessment, classifying the results into low heterogeneity (0–40%), moderate (40–70%) and high (70%-100%). Heterogeneity refers to the variability or differences in effect estimates across studies within a network [10]. The I² value was also computed with the “netmeta” function.

We performed a statistical analysis for design inconsistency and wherever possible for loop inconsistency. Design inconsistency stands for discrepancies in effect estimates between studies involving different composition of interventions. Loop inconsistency evaluates if direct and indirect evidence correspond with each other [10]. In order to locate hot spots of inconsistency, we performed within-design and between-design decomposition with Cochran’s Q statistics (significance level α = 0.05) (S5 File) [11]. For further graphical display, we also used if suitable net heat plots. The gray squares represent the degree of importance of one treatment comparison for the estimation of another treatment comparison and the color in the background illustrates the degree of inconsistency [11]. Distribution of direct and indirect evidence was illustrated in form of tables (S6 File).

To assess the robustness of our network analysis, we conducted several sensitivity analyses [9] in order to determine how our results are affected by different input variables.

Firstly, we included only studies with low and moderate risk of bias (S10 File) to examine the influence of different levels of risk of bias on our results. Additionally, we performed a second sensitivity analysis, including only studies in which single RAAS treatment was mandatory in the control group (S11 File). This excludes studies, in which only our 80% criterion regrading ACEi/ARB background treatment was fulfilled and not all patients were receiving ACEi/ARB treatment. By changing these specific input variables, we investigated how crucial our results are impacted by our decision to include studies without a mandatory ACEi/ARB background therapy. Finally, Finerenone was examined in two major clinical trials (FIGARO, FIDELIO). In our primary analysis we included these separately. However, as these trials had an identical design and were published also pooled in a prespecified analysis (FIDELITY), we repeated our analysis by treating the data from FIDELITY as single trial but excluding Figaro DKD and Fidelio DKD (S12 File).

Moreover, we rated the quality of evidence of each pairwise comparison according to GRADE (Grading of Recommendations Assessment, Development and Evaluation) [12]. The five following domains where implemented: Study limitations, Imprecision, Inconsistency, Indirectness and Publication bias. Every criterion for downgrading a comparison was predefined in a protocol, which may be found in the S7 File [13]. For the splitting into direct and indirect evidence we chose the back-calculation method, our subsequent results are presented in form of a forest plot for each comparison (S8 File).

Role of the funding source

There was no funding source for this study.

Results

Our literature search initially yielded 3489 publications, out of which 38 clinical trials were found fully eligible, in total including 42346 patients. Baseline characteristics of each study are presented in S1 File.

Mortality

Fig 2 shows the network structure for the primary outcome overall mortality, highlighting the larger number of clinical trials for SGLT2i, nsMRAs and ACEi + ARB. As the only drug class compared to the reference intervention (single ACEi/ARB), additional SGLT2 inhibitors significantly reduced the overall mortality (OR 0.81, 95% CI 0.70–0.95; p = 0.0080) (Fig 3). nsMRAs (Finerenone, Esaxerenone) vs. single ACEi/ARB (OR 0.89, 95% CI 0.74–1.06) and ACEi+ARB combination (OR 0.98, 95% CI 0.72–1.34) did not show any benefits. Overall mortality was reported in only two clinical trials for DRIs (vs. reference intervention) and did not reflect any positive effects (OR 1.05, 95% CI 0.82–1.34). For MRAs (vs. reference intervention), the endpoint was only reported by one clinical trial and was rated as low quality of evidence (GRADE). Comparison of these interventions with each other did not show any significant difference (Table 1). The primary endpoint overall mortality showed a heterogeneity of I^2 = 21.1%.

Fig 3 — OR = Odds ratio, 95%- CI = 95% Confidence interval, single ACEi/ARB = single Angiotensin-converting enzyme inhibitors or Angiotensin receptor blocker, ACEi+ARB = Angiotensin-converting enzyme inhibitors and Angiotensin receptor blocker combination, DRI = direct renin inhibitors, MRA = Mineralocorticoid receptor antagonists, nsMRA = non-steroidal Mineralocorticoid receptor antagonists, SGLT2i = Sodium glucose transporter inhibitors.

Table 1. Overall mortality and end stage kidney disease.

ACEi/ARB	1.22 (0.91–1.64)"			1.18 (0.99–1.40)#	1.46 (1.14–1.86)#
1.02 (0.75–1.39)#	ACEi+ARB			0.97 (0.69–1.36)*	1.19 (0.81–1.75)*
0.96 (0.75–1.22)"	0.63 (0.63–1.39)*	DRIs
4.46 (0.20–97.95)*	4.37 (0.20–97.61)§	4.67 (0.21–103.60)*	MRA
1.13 (0.94–1.35)#	1.11 (0.77–1.58)*	1.18 (0.87–1.60)*	0.25 (0.01–5.59)§	nsMRA	1.24 (0.92–1.67)*
1.23 (1.06–1.43)#	1.21 (0.86–1.70)*	1.29 (0.97–1.72)*	0.28 (0.01–6.10)§	1.09 (0.86–1.38)*	SGLT2i

Open in a new tab

For the upper triangle (ESKD = yellow color), OR lower than 1 favors the row defining intervention (e.g. for a pairwise comparison the row defining intervention is in an alphabetical order first)

For the lower triangle (overall mortality = blue color), OR lower than 1 favors the column defining intervention

To obtain the opposing OR you may calculate the reciprocal.

GRADE (#high confidence, "moderate confidence, *low confidence, §very low confidence)

ESKD

Eight trials reported the outcome ESKD for the drugs regimens ACEi+ARB, nsMRAs and SGLT2i. Only additional SGLT2 inhibitors on top of ACEi/ARB vs. single ACEi/ARB revealed a significant effectiveness in reducing the risk for ESKD (OR 0.69, 95% CI 0.54–0.88, p = 0.0025) (Fig 4). nsMRA did show a tendency to a reduced odds of ESKD (OR 0.85, 95% CI 0.71–1.01). Comparison of these interventions with each other did not show any significant difference (Table 1). The end stage kidney disease outcome had a heterogeneity of I^2 = 0%.

Fig 4 — OR = Odds ratio, 95%- CI = 95% Confidence interval, single ACEi/ARB = single Angiotensin-converting enzyme inhibitors or Angiotensin receptor blocker, ACEi+ARB = Angiotensin-converting enzyme inhibitors and Angiotensin receptor blocker combination, nsMRA = non-steroidal Mineralocorticoid receptor antagonists, SGLT2i = Sodium glucose transporter inhibitors.

Secondary endpoints

Only MRAs on top compared to single ACEi/ARB reduced albuminuria (SMD -0.58, 95% CI -0.85, -0.31) (S13 File). For Albuminuria we computed a high heterogeneity (I^2 = 73.9%).

The renal composite outcome consisting of sustained eGFR of less than 15 mL/ min/1.73 m2, sustained eGFR decline of 40% from baseline or kidney death was available for SGLT2i and nsMRAs, both reported by two trials each. In terms of improved outcomes, both interventions showed benefits (S14 File). Moreover, the indirect comparison of SGLT2i vs nsMRAs (OR 0.60, 95% CI 0.47–0.76) favors the SGLT2 inhibitors as the more effective drug class, which was rated as moderate quality of evidence (GRADE).

Safety endpoints

We also explored the endpoint acute kidney injury (Fig 5) which was reported by a minimum of two studies for each intervention and altogether by 19 clinical trials. ACEi+ARB combination therapy demonstrated a substantially elevated risk for acute kidney injury (OR 1.69, 95% CI 1.28–2.22). Notably, SGLT2i (OR 0.93, 95% CI 0.0.77–1.12) and nsMRAs (OR 0.97; 95% CI 0.80–1.16) did not have any negative effects on renal function.

Hyperkalemia was reported by 27 trials, being one of the most frequently reported adverse events (Fig 2). In comparison to single ACEi/ARB (Fig 4), nsMRAs, MRAs and ACEi+ARB combination were associated with an increased incidence of hyperkalemia. The indirect comparison of nsMRAs vs MRAs (0.58, 95% CI 0.28–1.19) reflects an attenuated risk for hyperkalemia on the side of nsMRAs, although not significant and rated as low quality of evidence (GRADE). Hyperkalemia and acute kidney injury are presented in an effect estimate table (Table 2).

Table 2. Hyperkalemia and acute kidney injury.

ACEi/ARB	0.55 (0.39–0.78)#	0.72 (0.50–1.03)"	0.25 (0.13–0.48)"	0.43 (0.32–0.57)#	1.17 (0.83–1.65)#
0.59 (0.45–0.78)"	ACEi+ARB	1.31 (0.79–2.20)*	0.45 (0.23–0.91)#	0.78 (0.50–1.23)"	2.14 (1.31–3.49)"
0.81 (0.55–1.21)"	1.37 (0.85–2.22)*	DRIs	0.34 (0.16–0.73)*	0.60 (0.37–0.95)"	1.64 (0.99–2.69)*
0.64 (0.25–1.63)"	1.07 (0.42–2.75)#	0.78 (0.28–2.18)*	MRA	1.73 (0.84–3.58)*	4.75 (2.25–10.01)*
1.04 (0.86–1.25)#	1.74 (1.25–2.43)*	1.27 (0.82–1.97)*	1.63 (0.62–4.25)*	nsMRA	2.74 (1.76–4.28)"
1.08 (0.90–1.30)#	1.82 (1.30–2.53)*	1.33 (0.86–2.05)*	1.69 (0.65–4.43)*	1.04 (0.80–1.35)"	SGLT2i

Open in a new tab

For the upper triangle (hyperkalemia = yellow color), OR lower than 1 favors the row defining intervention

For the lower triangle (acute kidney injury = blue color), OR lower than 1 favors the column defining intervention

To obtain the opposing OR you may calculate the reciprocal.

GRADE (#high confidence, "moderate confidence, *low confidence, §very low confidence)

SGLT2is and DRIs (Fig 5) were found to be the only two interventions to significantly elevate the risk for hypotension, (OR 1.27, 95% CI 1.01–1.59 and OR 1.59, 95% CI 1.10–2.29) respectively. In addition, nsMRAs were also associated with an increased risk (OR 1.26, 95% CI 0.92–1.71), but this finding was not significant.

Sensitivity analyses

All three sensitivity analyses displayed a similar trend as without any exclusions (S10–S12 Files). However, the effect of nsMRA on ESKD was somewhat larger resulting in a significant result (OR 0.80, 95% CI 0.64–0.99).

Furthermore, we generally computed for all endpoints, except for albuminuria, a low heterogeneity and the Q statistics decomposition was not noticeable.

Clinical trials comprising comparisons of ACEI+ARB, MRAs and DRIs were mostly older than five years and tended to be assessed as high risk/some concerns (70%) through Cochrane´s RoB 2 tool. In contrast, SGLT2i and nsMRAs are represented mainly by recent studies with low risk of bias (appr. 85%) (S9 File). Moreover, we calculated the interrater reliability kappa on screening level (kappa = 0.88) and on abstract level (kappa = 0.89).

Discussion

This network meta-analysis comprises data from 42346 patients with DKD. This high number allows a comprehensive analysis on the comparative effectiveness of various drug regimens. On top of treatment with ACEi or ARB only SGLT2 inhibition caused a decrease in mortality and in the incidence of ESKD. Therefore, SGLT2i should be an essential part of the treatment of each patient with DKD in addition to ACEi or ARB. We for the first time compared all interventions meant to reduce progression of DKD and have been used from nephrologists during the last two decades.

SGLT2i were introduced into treatment of DM primarily due to their glucosuric effects leading to improved glucose control [14]. Regarding DKD, this effect is only one of the potential beneficial effects of SGLT2 inhibition. In the complex pathophysiology of DKD SGLT2i exert multiple positive effects leading to reduced progression of kidney disease [15, 16]. Due to the blockade of SGLT2 less glucose and sodium is transported into the tubular cell, which in turn reduces tubular energy consumption of the tubular cell by reducing the workload to the basal Na/K ATP-ase pump and thereby reducing tubular hypoxia [17]. In addition, due to the increased amount of sodium approaching the juxtaglomerular apparatus, tubular glomerular feedback decreases renal plasma flow, glomerular pressure and glomerular filtration rate [18] leading to reduced glomerular damage. Additional positive effects of SGLT2i are their antihypertensive [19], anti-inflammatory [20] and antifibrotic properties [21]. The positive effects of SGLT2i in DKD have been comprehensively reviewed, recently [22]. Regarding safety, concerns about acute kidney injury are unfounded and SGLT2i even showed a propensity for reducing its incidence. A meta-analysis confirmed this finding, by obtaining a significantly reduced risk for the occurrence of acute kidney injury using SGLT2i in diabetic patients [23].

Blocking the RAAS has been standard treatment of DKD. Based on the positive effects of ACEI and ARBs on DKD and overall cardiovascular risk [24] and the obvious need for further improvement of treatment, lot of effort was put in exploring interventions providing a more complete blockade of the RAAS. To provide this in addition to ACEi/ARB treatment MRAs, dual blockade with ACEi and ARB and use of ACEi/ARB in combination with aliskiren—the only available DRI—have been explored [5, 25, 26]. Unfortunately, the dual blockade with ACEi and ARB as well as the addition of aliskiren raised major safety concerns in large randomized, controlled trials [5, 26] which led to a restricted use of these two combination therapies. This is reflected by the results of our analysis, in which these treatments did not improve efficacy endpoints but showed increased safety concerns regarding acute kidney injury (ACEi + ARB) hyperkalemia (ACEi + ARB, DRIs) and hypotension (DRI).

Until recently MRAs besides Glucagon-like peptide-1 receptor agonists were the only treatment alternative for patients with progression of DKD and/or gross proteinuria despite ACEi/ARB and SGLT2i treatment. However, steroidal MRAs, i.e. spironolactone and eplerenone, were disproportionally underrepresented for sufficient data on hard endpoints in our analysis. Due to a small number of included patients and short follow up periods, most studies with MRAs were limited to albuminuria as surrogate endpoint. This suggests a positive effect on progression of DKD, but this has not been proven neither in a single study nor in our analysis. On the downside, MRAs have an unfavorable side effect profile. We can show that the odds for hyperkalemia are fourfold raised compared to ACEi/ARB monotherapy.

A further evolution of MRA, nsMRAs are the most recently developed pharmacological agents acting on the RAAS system. They block the mineralocorticoid receptor more selectively and potently than steroidal MRA (Eplerenone, Spironolactone) [27]. Due to this modified mode of action, a lower rate of hyperkalemia and a better end-organ protection were presumed [28]. Some of these preclinical assumptions were confirmed by large clinical trials [29, 30]. In our analysis, they were associated with a lower incidence of the renal composite outcome (compared to single ACEi/ARB). In comparison to the conventional single ACEi/ARB the odds for hyperkalemia were raised, but compared to steroidal MRA, they tended to show a lower incidence. Our results are not confirmative for this suggested correlation and require more studies for validation. Unlike ACEi+ARB combination or MRAs, nsMRA did not cause higher rates of acute kidney injury (compared to single ACEi/ARB). Whereas SGLT2i have already emerged as standard treatment of diabetic kidney disease [31], nsMRA are recommended in patients with persisting microalbuminuria despite sufficient ACEi/ARB treatment as additional treatment in the current consensus report of the American Diabetes Association and Kidney Disease: Improving Global Outcomes (KDIGO) [32] and the KDIGO 2022 Clinical Practice Guideline for Diabetes management in Chronic Kidney Disease [33]. First analyses of existing data are supporting this view [34, 35] and an ongoing study is exploring the combined effect of finerenone and empagliflozin [36].

Whereas albuminuria has been shown to be associated with more rapid progression of DN [37] and higher cardiovascular risk [38], interventions lowering albuminuria have not uniformly been successful in lowering the incidence of hard endpoints. For example, DRIs reduced albuminuria compared to single ACEi/ARB. The premature stop of the large clinical trial ALTITUDE, where a DRI (Aliskiren) failed to show benefits in particular for hard renal endpoints, reflects unfavorable effects of DRI [26]. Although more evidence emerges supporting albuminuria as a valid surrogate parameter for chronic kidney diseases [38], it remains disputable. Our results did not advocate use of albuminuria as a surrogate endpoint for renal outcomes as the most effective drug class with regard to renal outcome, i.e. SGLT2i, showed the least effect on albuminuria. Notably, our result concerning albuminuria must be interpreted cautiously owing to its higher heterogeneity and raised level of inconsistency.

Limitations of our network meta-analysis were not uniformly reported endpoints like hyperkalemia, ranging from no specified cut off levels to no exact definition. Moreover, we could not consider different doses of each drug and differences in ACEi/ARB doses. In particular, most clinical trials including SGLT2i did not require the maximal labeled dose of ACEi/ARB. This however was the case in the studies including nsMRA [39]. This hampers the comparability of these drug classes. In addition, SGLT2i studies mainly had renal secondary endpoints and mostly included patients with an eGFR above 45 mL/min/1.73 m². Due to sparse data covering DRI and particularly MRA for our primary endpoints, we may offer only limited clarity about them. In general, the reporting of different renal endpoints by each clinical trial hampered the power of our NMA. For albuminuria we computed a high heterogeneity, which we assume to be mainly caused from different level of albuminuria ranging from microalbuminuria to macroalbuminuria and varying follow up periods. Several other network meta-analyses have been performed on the issue of outcome of DKD recently. However, the focus of these analyses was different from ours. Cao et al. compared SGLT2i, GLP-1RAs and dipeptidyl peptidase 4 inhibitors [40] and Ghosal et al. SGLT2i, Glucagon-like peptide– 1 receptor antagonists and finerenone [41] regarding renal endpoints. None of these focused on classical antiproteinuric agents.

In conclusion, SGLT2i in our analysis was the only drug class showing beneficial effects on top of ACEi/ARB treatment regarding mortality and end stage kidney disease. nsMRA were based on less data but diminished the risk for our renal composite outcome without safety concerns. Our data support the actual ADA and KDIGO guidelines, which suggest that all three drug classes (ACEi/ARB, SGLT2i and nsMRA) are needed in diabetic patients with albuminuria.

Supporting information

S1 File. Baseline characteristics.

(PDF)

Click here for additional data file.^{(228.8KB, pdf)}

S2 File. Citation list.

(PDF)

Click here for additional data file.^{(89.2KB, pdf)}

S3 File. Prisma statements 2020.

(PDF)

Click here for additional data file.^{(177KB, pdf)}

S4 File. Search strategy.

(PDF)

Click here for additional data file.^{(30.7KB, pdf)}

S5 File. Tests of heterogeneity (within designs) and inconsistency (between designs) with Cochran’s Q statistics.

(PDF)

Click here for additional data file.^{(83.2KB, pdf)}

S6 File. Distribution of direct and indirect evidence.

(PDF)

Click here for additional data file.^{(769.8KB, pdf)}

S7 File. GRADE protocol.

(PDF)

Click here for additional data file.^{(106.2KB, pdf)}

S8 File. Splitting into direct and indirect evidence.

(PDF)

Click here for additional data file.^{(1MB, pdf)}

S9 File. Risk of bias (given in excel).

(PDF)

Click here for additional data file.^{(320.8KB, pdf)}

S10 File. Sensitivity analysis (RoB).

(PDF)

Click here for additional data file.^{(30.2KB, pdf)}

S11 File. Sensitivity analysis (single ACEi/ARB mandatory in control groups).

(PDF)

Click here for additional data file.^{(29.7KB, pdf)}

S12 File. Sensitivity analysis (replacing FIGARO and FIDELIO bei FIDELITY).

(PDF)

Click here for additional data file.^{(86.6KB, pdf)}

S13 File. Albuminuria, renal composite outcome (sustained eGFR <15 mL/ min/1.73 m2, sustained eGFR decline of 40% from baseline or kidney death).

(PDF)

Click here for additional data file.^{(79.8KB, pdf)}

S14 File. Renal composite outcome (sustained eGFR <15 mL/ min/1.73 m2, sustained eGFR decline of 40% from baseline or kidney death).

(PDF)

Click here for additional data file.^{(62.3KB, pdf)}

S15 File. R-code.

(PDF)

Click here for additional data file.^{(36.6KB, pdf)}

S16 File. Data sheets.

(PDF)

Click here for additional data file.^{(202.1KB, pdf)}

S17 File. GRADE for renal composite outcome.

(PDF)

Click here for additional data file.^{(72.6KB, pdf)}

S18 File. GRADE for hypotension.

(PDF)

Click here for additional data file.^{(77.8KB, pdf)}

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

BMWS received honoraria for lectures/consulting from AstraZeneca and Bayer Vital. These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020;396(10258):1204–22. doi: 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Li H, Lu W, Wang A, Jiang H, Lyu J. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: Estimates from Global Burden of Disease 2017. J Diabetes Investig. 2021;12(3):346–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Palmer SC, Mavridis D, Navarese E, Craig JC, Tonelli M, Salanti G, et al. Comparative efficacy and safety of blood pressure-lowering agents in adults with diabetes and kidney disease: A network meta-analysis. The Lancet. 2015;385(9982):2047–56. doi: 10.1016/S0140-6736(14)62459-4 [DOI] [PubMed] [Google Scholar]
4.Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Vol. 120, Nephron: —Clinical Practice. 2012. [DOI] [PubMed] [Google Scholar]
5.Fried LF, Emanuele N, Zhang JH, Brophy M, Conner TA, Duckworth W, et al. Combined angiotensin inhibition for the treatment of diabetic nephropathy. N Engl J Med. 2013. Nov;369(20):1892–903. doi: 10.1056/NEJMoa1303154 [DOI] [PubMed] [Google Scholar]
6.Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJLL, Charytan DM, et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. New England journal of medicine. 2019. Jun;380(24):2295‐2306. doi: 10.1056/NEJMoa1811744 [DOI] [PubMed] [Google Scholar]
7.Harrer M, Cuijpers P, Furukawa TA, Ebert DD. Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL and London: Chapman & Hall/CRC Press; 2021. ISBN 978-0-367-61007-4. [Google Scholar]
8.Salanti G, Del Giovane C, Chaimani A, Caldwell DM, Higgins JPT. Evaluating the Quality of Evidence from a Network Meta- Analysis. 2014;9(7). [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ W V (editors). Cochrane Handbook for Systematic Reviews of Interventions. 2nd edition. Chichester (UK): John Wiley & Sons; 2019. [Google Scholar]
10.Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta‐analysis: concepts and models for multi‐arm studies. Res Synth Methods. 2012. Jun;3(2):98–110. doi: 10.1002/jrsm.1044 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Krahn U, Binder H, König J. A graphical tool for locating inconsistency in network meta-analyses. BMC Med Res Methodol. 2013;13(1). doi: 10.1186/1471-2288-13-35 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Puhan MA, Schünemann HJ, Murad MH, Li T, Brignardello-Petersen R, Singh JA, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ (Online). 2014;349(September):1–10. [DOI] [PubMed] [Google Scholar]
13.Brignardello-Petersen R, Bonner A, Alexander PE, Siemieniuk RA, Furukawa TA, Rochwerg B, et al. Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis. J Clin Epidemiol. 2018;93:36–44. [DOI] [PubMed] [Google Scholar]
14.DeFronzo RA, Reeves WB, Awad AS. Pathophysiology of diabetic kidney disease: impact of SGLT2 inhibitors. Nat Rev Nephrol. 2021;17(5):319–34. doi: 10.1038/s41581-021-00393-8 [DOI] [PubMed] [Google Scholar]
15.Wanner C, Inzucchi SE, Lachin JM, Fitchett D, von Eynatten M, Mattheus M, et al. Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes. New England Journal of Medicine. 2016;375(4):323–34. doi: 10.1056/NEJMoa1515920 [DOI] [PubMed] [Google Scholar]
16.McMurray JJV, Wheeler DC, Stefánsson B V., Jongs N, Postmus D, Correa-Rotter R, et al. Effects of Dapagliflozin in Patients With Kidney Disease, With and Without Heart Failure. JACC Heart Fail. 2021;9(11):807–20. [DOI] [PubMed] [Google Scholar]
17.Layton AT, Vallon V, Edwards A. Predicted consequences of diabetes and SGLT inhibition on transport and oxygen consumption along a rat nephron. Am J Physiol Renal Physiol. 2016;310(11):F1269–83. doi: 10.1152/ajprenal.00543.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Thomson SC, Rieg T, Miracle C, Mansoury H, Whaley J, Vallon V, et al. Acute and chronic effects of SGLT2 blockade on glomerular and tubular function in the early diabetic rat. Am J Physiol Regul Integr Comp Physiol. 2012;302(1):75–83. doi: 10.1152/ajpregu.00357.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.DeFronzo RA, Norton L, Abdul-Ghani M. Renal, metabolic and cardiovascular considerations of SGLT2 inhibition. Nat Rev Nephrol. 2017;13(1):11–26. doi: 10.1038/nrneph.2016.170 [DOI] [PubMed] [Google Scholar]
20.Han JH, Oh TJ, Lee G, Maeng HJ, Lee DH, Kim KM, et al. The beneficial effects of empagliflozin, an SGLT2 inhibitor, on atherosclerosis in ApoE −/− mice fed a western diet. Diabetologia. 2017;60(2):364–76. doi: 10.1007/s00125-016-4158-2 [DOI] [PubMed] [Google Scholar]
21.Vallon V, Gerasimova M, Rose MA, Masuda T, Satriano J, Mayoux E, et al. SGLT2 inhibitor empagliflozin reduces renal growth and albuminuria in proportion to hyperglycemia and prevents glomerular hyperfiltration in diabetic Akita mice. Am J Physiol Renal Physiol. 2014;306(2):194–204. doi: 10.1152/ajprenal.00520.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Ni L, Yuan C, Chen G, Zhang C, Wu X. SGLT2i: beyond the glucose—lowering effect. Cardiovasc Diabetol. 2020;1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Menne J, Dumann E, Haller H, Schmidt BMW. Acute kidney injury and adverse renal events in patients receiving SGLT2-inhibitors: A systematic review and meta-analysis. PLoS Med. 2019;16(12):1–13. doi: 10.1371/journal.pmed.1002983 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Schmieder RE, Hilgers KF, Schlaich MP, Schmidt BM. Renin-angiotensin system and cardiovascular risk. Lancet. 2007;369(9568):1208–19. doi: 10.1016/S0140-6736(07)60242-6 [DOI] [PubMed] [Google Scholar]
25.van den Meiracker AH, Baggen RGAG, Pauli S, Lindemans A, Vulto AG, Poldermans D, et al. Spironolactone in type 2 diabetic nephropathy: Effects on proteinuria, blood pressure and renal function. J Hypertens. 2006. Nov;24(11):2285–92. doi: 10.1097/01.hjh.0000249708.44016.5c [DOI] [PubMed] [Google Scholar]
26.Parving HH, Brenner BM, McMurray JJ V, de Zeeuw D, Haffner SM, Solomon SD, et al. Cardiorenal end points in a trial of aliskiren for type 2 diabetes. N Engl J Med. 2012. Dec;367(23):2204–13. doi: 10.1056/NEJMoa1208799 [DOI] [PubMed] [Google Scholar]
27.Grune J, Beyhoff N, Smeir E, Chudek R, Blumrich A, Ban Z, et al. Selective mineralocorticoid receptor cofactor modulation as molecular basis for finerenone’s antifbrotic activity. Hypertension. 2018;71(4):599–608. [DOI] [PubMed] [Google Scholar]
28.Kolkhof P, Delbeck M, Kretschmer A, Steinke W, Hartmann E, Bärfacker L, et al. Finerenone, a novel selective nonsteroidal mineralocorticoid receptor antagonist protects from rat cardiorenal injury. J Cardiovasc Pharmacol. 2014. Jul;64(1):69–78. doi: 10.1097/FJC.0000000000000091 [DOI] [PubMed] [Google Scholar]
29.Bakris GL, Filippatos G, Bakris GL, Agarwal R, Anker SD, Pitt B, et al. Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. New England journal of medicine. 2020;383(23):2219‐2229. doi: 10.1056/NEJMoa2025845 [DOI] [PubMed] [Google Scholar]
30.Pitt B, Filippatos G, Agarwal R, Anker SD, Bakris GL, Rossing P, et al. Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. New England Journal of Medicine. 2021. Aug 28;385(24):2252–63. doi: 10.1056/NEJMoa2110956 [DOI] [PubMed] [Google Scholar]
31.Rossing P, Caramori ML, Chan JCN, Heerspink HJL, Hurst C, Khunti K, et al. Executive summary of the KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease: an update based on rapidly emerging new evidence. Kidney Int. 2022;102(5):990–9. doi: 10.1016/j.kint.2022.06.013 [DOI] [PubMed] [Google Scholar]
32.de Boer IH, Khunti K, Sadusky T, Tuttle KR, Neumiller JJ, Rhee CM, et al. Diabetes management in chronic kidney disease: a consensus report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Vol. 102, Kidney international. United States; 2022. p. 974–89. [DOI] [PubMed] [Google Scholar]
33.Rossing P, Caramori ML, Chan JCN, Heerspink HJL, Hurst C, Khunti K, et al. Executive summary of the KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease: an update based on rapidly emerging new evidence. Vol. 102, Kidney international. United States; 2022. p. 990–9. [DOI] [PubMed] [Google Scholar]
34.Rossing P, Filippatos G, Agarwal R, Anker SD, Pitt B, Ruilope LM, et al. Finerenone in Predominantly Advanced CKD and Type 2 Diabetes With or Without Sodium-Glucose Cotransporter-2 Inhibitor Therapy. Kidney Int Rep. 2022;7(1):36–45. doi: 10.1016/j.ekir.2021.10.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Goulooze SC, Heerspink HJL, van Noort M, Snelder N, Brinker M, Lippert J, et al. Dose–Exposure–Response Analysis of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone on UACR and eGFR: An Analysis from FIDELIO-DKD. Clin Pharmacokinet. 2022;61(7):1013–25. doi: 10.1007/s40262-022-01124-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Green JB, Mottl AK, Bakris G, Heerspink HJL, Mann JFE, McGill JB, et al. Design of the COmbinatioN effect of FInerenone anD EmpaglifloziN in participants with chronic kidney disease and type 2 diabetes using an UACR Endpoint study (CONFIDENCE). Nephrology Dialysis Transplantation. 2022;(June):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.De Zeeuw D, Remuzzi G, Parving HH, Keane WF, Zhang Z, Shahinfar S, et al. Proteinuria, a target for renoprotection in patients with type 2 diabetic nephropathy: Lessons from RENAAL. Kidney Int. 2004;65(6):2309–20. doi: 10.1111/j.1523-1755.2004.00653.x [DOI] [PubMed] [Google Scholar]
38.Waijer SW, Gansevoort RT, Heerspink HJL. Change in albuminuria as a surrogate endpoint. Curr Opin Nephrol Hypertens. 2019;28(6):519–26. doi: 10.1097/MNH.0000000000000541 [DOI] [PubMed] [Google Scholar]
39.Bakris GL, Filippatos G, Agarwal R, Anker SD, Pitt B, et al. Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. New England journal of medicine. 2020;383(23):2219‐2229. doi: 10.1056/NEJMoa2025845 [DOI] [PubMed] [Google Scholar]
40.Cao H, Liu T, Wang L, Ji Q. Comparative efficacy of novel antidiabetic drugs on cardiovascular and renal outcomes in patients with diabetic kidney disease: A systematic review and network meta-analysis. Diabetes Obes Metab. 2022;24(8):1448–57. doi: 10.1111/dom.14702 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Ghosal S, Sinha B. Assessing the Effects of Modern Renoprotective Agents in Preventing Progression of Renal Composite Outcomes in Patients with Type 2 Diabetes: A Network Meta-analysis. Diabetes Therapy. 2022. doi: 10.1007/s13300-022-01359-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0293183.r001

Decision Letter 0

Licy Yanes Cardozo

20 Mar 2023

PONE-D-23-02576Treatment of diabetic kidney disease. A network meta-analysisPLOS ONE

Dear Dr. Büttner,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 04 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Licy Yanes Cardozo

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include a separate caption for each figure in your manuscript.

3. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files

4. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors performed a meta-analysis to compare drug treatments of DKD by means of a systemic review and a network meta-analysis. As primary endpoints, they defined: overall mortality and end-stage kidney disease, as secondary endpoints: renal composite outcome and albuminuria and as safety endpoints: acute kidney injury, hyperkalemia and hypotension. They used a random effects model and computed the overall effect estimates using R4.1. They found 38 clinical trials eligible for review. They found that only SGLT2 inhibitors reduced both overall mortality and end-stage kidney disease when used with single ACEi/ARB compared ACEi/ARB alone. The authors attempted to compared SGLT2 inhibitors to NS-MRA and found SGLT2 inhibitors superior in this regard. Concerning safety endpoints, nsMRA and SGLT2i showed benefits compared to the others. The authors conclude that SGLT2i showed clear beneficial effects on top of ACEi/ARB treatment regarding mortality and end-stage kidney disease and by that reconfirmed its position as first-line treatment option in the KDIGO Guideline 2022. nsMRA reduced the odds for a combined renal endpoint and did not raise any safety concerns, justifying its potential application.

This is a reasonably well-done meta-analysis but the reality is that all clinical trials are not the same and go beyond randomization and double-blind protocols. One of the main distinctions between studies done with NS-MRA and SGLT2 inhibitors is that maximal dosing of ACEI/ARB was NOT mandated in the SGLT2i studies. I can assure you this was an issue not discussed with the design of the renal SGLT2 I trials. This will affect outcomes as has been shown in analyses evaluating lower doses of RAS blockade in people from those trials (Epstein, M., et al. Am J Manag Care 2015; 21(11 Suppl): S212-220; and data within the 2001 Brenner et.al. and Lewis et.al. NEJM papers). Moreover, many of the original trials in people with eGFR above 45 were focused on glucose control and not renal outcomes. The 3 trials CREDENCE, DAPA-CKD and Empa-KIDNEY did focus on appropriately powered renal and the secondary endpoint all-cause morality.

The statement about KDIGO guidelines is also not quite correct. The Am Diabetes Assoc guidelines 2023 is very clear that SGLT2 inhibitors are mandated if advanced CKD or heart failure is present irrespective of glycemic control and KDIGO is mirroring that, however, NS-MRA are also highly supported to be used with SGLT2 inhibitors is albuminuria is present as they have shown and the authors appropriately state protection against ESRD and reduction in heart failure hospitalization. Thus, like in heart failure, all 3 are suggested for treatment in this case and thus, the wording should be less absolute and imply that all 3 are needed if albuminuria is present which is what all studies of CKD were. Actually, the comment about guidelines should be excluded or include ADA guidelines with a less absolute statement to include both agents.

Reviewer #2: 1. There are a few errors or incorrect statements in the "key learning points" section:

The statement "New clinical trials with SGLT2i and nsMRA emerged" is not accurate as clinical trials on these drug classes have been ongoing for several years.

The statement "direct comparisons between these two drug classes and comparison to older drug classes/combination (MRA, DRI, ACEi+ARB combination) are missing" is incorrect as the study mentioned in the background section actually does include comparisons between these drug classes.

The statement "For MRA and DRI existed no sufficient hard outcome data" is incorrect as there have been clinical trials on these drug classes that have reported hard outcome data.

The statement "DRI in addition to ACEi/ARB or ACEi+ARB combinations are not recommended" is not accurate as some clinical guidelines do recommend the use of DRI in certain cases.

The statement "the usage of MRA is questionable due to missing hard endpoint data" is not entirely accurate as there are some clinical trials that have reported hard endpoint data on the use of MRA, although more data may be needed to fully assess their efficacy and safety.

2. There are no major errors or incorrect fundamentals in the introduction. However, a few minor improvements could be made to enhance clarity and readability, as follows:

In the first sentence, "detrimental" could be replaced with "debilitating" or "harmful" to avoid repetition of the same word in the sentence.

In the second sentence, it could be clarified that the increasing incidence of DKD is a global trend, not limited to a specific region or country.

In the third sentence, "considerable advantages" could be elaborated upon to provide more specific details, such as reduction in proteinuria, preservation of kidney function, or decrease in cardiovascular events.

In the fourth sentence, "mainly dual RAAS blockade" could be revised to "primarily dual RAAS blockade" for greater precision and concision.

In the fifth sentence, it could be helpful to briefly define SGLT2i and nsMRA for readers who may not be familiar with these terms, or to include a footnote or reference to explain them.

In the sixth sentence, "best suitable treatment" could be changed to "optimal treatment strategy" for greater clarity and professionalism. Additionally, it may be useful to specify the patient population or criteria for inclusion in the analysis, such as stage of DKD or presence of comorbidities.

3. Method: there are some errors and potential areas of improvement:

There is a typo in the PRISMA statement (it should be PRISMA 2020, not 2010).

It would be helpful to state the date range of the search for articles in Medline and clinicaltrials.gov.

The meaning of "nsMRAs" is unclear and should be defined.

The exclusion of studies with insufficient information should be clarified (e.g., what was considered "insufficient"?).

The description of the screening process could be more detailed, including how conflicts were resolved.

It would be helpful to specify the types of missing data and how study investigators were contacted.

The description of outcomes could be more succinct and organized.

The meaning of "comparison adjusted funnel plots" is unclear and should be defined.

It would be helpful to specify the statistical significance level used for Cochran's Q statistics.

The rationale for the sensitivity analyses should be explained more clearly.

There are some grammar and syntax errors (e.g., "We considered 'background treatment with single ACEi/ARB' as fulfilled," "Furthermore, image extraction software... was used for relevant outcome data presented only in form of figures without numerical values.").

4. Results:

In the sentence "Merely additional SGLT2 inhibitors on top of ACEi/ARB vs. single ACEi/ARB revealed a significant effectiveness in reducing the risk for ESKD," "Merely" should be replaced with "Only" or "Just".

In the sentence "Comparison of the interventions with each other did not show any significant difference (Table 1).", "Table

In the sentence "The sensitivity analysis, including only clinical trials stating single ACEi/ARB as active control displayed the same trend as without any pre...", the sentence is incomplete and needs to be finished.

It will be better to show kappa for the selection and data extraction. Please show the data of kappa of agreement during the systematic searches. How disagreements were solved during the systematic search among two independent reviewers?

5. Discussion:

There are a few errors or incorrect fundamentals in the discussion:

The statement that SGLT2i is the only drug that caused a decrease in mortality and the incidence of ESKD is not correct. The discussion did not mention which drugs were compared in the network meta-analysis, but it is unlikely that SGLT2i was the only drug that showed a positive effect. This statement needs to be revised.

The discussion states that the positive effect of SGLT2i on DKD has been comprehensively reviewed recently, citing a reference (11). However, the reference provided does not seem to be related to the positive effects of SGLT2i on DKD. This needs to be clarified.

The discussion claims that dual blockade with ACEi and ARB and the addition of aliskiren have been completely banned due to major safety concerns. This is not entirely correct. While some studies have shown increased risks of adverse events with these combinations, they are not completely banned and may still be used in certain circumstances.

The discussion suggests that MRAs have been the only treatment alternative for patients with progression of DKD and/or gross proteinuria despite ACEi/ARB and SGLT2i treatment. This is not entirely accurate as there are other treatment options, such as GLP-1 receptor agonists, which have shown promising results in clinical trials.

The discussion states that nsMRAs tend to show a lower incidence of hyperkalemia compared to steroidal MRA. However, the evidence is not conclusive, and further studies are needed to confirm this claim.

The discussion claims that ongoing studies are exploring the combined effect of finerenone and empagliflozin. However, the reference provided for this claim is outdated, and newer studies have been published on this topic.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Nov 2;18(11):e0293183. doi: 10.1371/journal.pone.0293183.r002

Author response to Decision Letter 0

4 May 2023

We have addressed each point raised by the reviewers and believe the manuscript is now appropriate for publication in PLOS ONE.

More details may be found in the file "Response to Reviewers".

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(37.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0293183.r003

Decision Letter 1

Licy Yanes Cardozo

23 May 2023

PONE-D-23-02576R1Treatment of diabetic kidney disease. A network meta-analysisPLOS ONE

Dear Dr. Büttner,

Please submit your revised manuscript by Jul 07 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Licy Yanes Cardozo

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Associated Editor note: Please ensure that all relevant clinical trial are included in the current from of the metanalysis.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have failed to include the FIGARO trial Pitt B et.al. NEJM and more importantly the FIDELITY individual patient analysis that includes over 13000 patients-this is NOT a meta-analysis as it is all patients used from two trials with the same protocol and centers but different inclusion criteria Agarwal R et.al. Eur Heart J 2022. Additionally data from this analysis does show a benefit on all cause mortality. Moreover, not sure why Exacernone is included as there No trial data other than albuminuria in diabetic nephropathy. Hence, this is not a complete analysis.

Reviewer #2: I reviewed the revised manuscript and the response to reviewers' comments. Revised Manuscript is well written. All comments have been addressed

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2023 Nov 2;18(11):e0293183. doi: 10.1371/journal.pone.0293183.r004

Author response to Decision Letter 1

2 Jul 2023

Dear editor,

We thank the reviewers again for their efforts.

We have addressed the additional comment of Reviewer #1.

Reviewer #1:

The authors have failed to include the FIGARO trial Pitt B et.al. NEJM and more importantly the FIDELITY individual patient analysis that includes over 13000 patients-this is NOT a meta-analysis as it is all patients used from two trials with the same protocol and centers but different inclusion criteria Agarwal R et.al. Eur Heart J 2022. Additionally data from this analysis does show a benefit on all cause mortality.

In our primary analysis we had separately included the FIGARO and FIDELIO trials (compare supplemental table 1). We did not include the FIDELITY pooled analysis, as this uses the same data. Now, we performed an additional sensitivity analysis treating FIDELITY as one trial and included it into the analysis, while we omitted FIGARO and FIDELIO. This analysis (given as new supplemental figure 15) gives us essentially the same results.

Supplemental figure 12 a OR of mortality with using FIDELITY as a single study

Supplemental figure 12 b OR of ESKD with using FIDELITY as a single study

In addition we mentioned this analysis in the paragraph of the methods section on sensitivity analyses on page 10, paragraph 2

Finerenone was examined in two major clinical trials (FIGARO, FIDELIO). In our primary analysis we included these separately. However, as these trials had an identical design and were published also pooled in a prespecified analysis (FIDELITY), we repeated our analysis by treating the data from FIDELITY as single trial but excluding Figaro DKD and Fidelio DKD.

We changed the paragraph on sensitivity analyses in the results section (page 17, second paragraph):

All three sensitivity analyses displayed a similar trend as without any exclusions (S10, S11, S 12). However, the effect of nsMRA on ESKD was somewhat larger in the analysis treating FIDELITY as single trial resulting in a significant result (OR 0.80, 95% CI 0.64 - 0.99).

In addition, we rephrased a sentence in the discussion on page 20 first paragraph:

Whereas SGLT2i have already emerged as standard treatment of diabetic kidney disease [28] nsMRA are recommended in patients with persisting microalbuminuria despite sufficient ACEi/ARB treatment as additional treatment in the current consensus report of the American Diabetes Association and Kidney Disease: Improving Global Outcomes (KDIGO) (29) and the KDIGO 2022 Clinical Practice Guideline for Diabetes management in Chronic Kidney Disease (30).

Moreover, not sure why Exacernone is included as there No trial data other than albuminuria in diabetic nephropathy.

We included esaxerenone as the study mentioned fulfilled our inclusion criteria and contributed to a secondary endpoint of our analysis. In order to show the complete evidence, we would like to leave this in.

Hence, this is not a complete analysis.

We do not agree. Our analysis is complete

Attachment

Submitted filename: rebuttal letter_2_BS-2.docx

Click here for additional data file.^{(76.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0293183.r005

Decision Letter 2

Licy Yanes Cardozo

19 Jul 2023

PONE-D-23-02576R2Treatment of diabetic kidney disease. A network meta-analysisPLOS ONE

Dear Dr. Büttner,

Please submit your revised manuscript by Sep 02 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Licy Yanes Cardozo

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: no further comments needed all revisions are appropriate

Of initial 3489 publications, 38 clinical trials were found eligible, in total including 42346

patients. Concerning the primary endpoints overall mortality and end stage kidney

disease, SGLT2i on top of single ACEi/ARB compared to single ACEi/ARB was the

only intervention significantly reducing the odds of mortality (OR 0.81, 95%CI 0.70-

0.95) and end-stage kidney disease (OR 0.69, 95%CI 0.54- 0.88). The indirect

comparison of nsMRA vs SGLT2i in our composite endpoint suggests a superiority of

SGLT2i (OR 0.60, 95%CI 0.47- 0.76).

Concerning safety endpoints, nsMRA and SGLT2i showed benefits compared to the

others.

Conclusions

As the only drug class, SGLT2i showed clear beneficial effects on top of ACEi/ARB

treatment regarding mortality and end stage kidney disease and by that reconfirmed its

position as first line treatment option in the KDIGO Guideline 2022. nsMRA reduced

the odds for a combined renal endpoint and did not raise any safety concerns,

justifying its potential application.

Reviewer #2: Introduction

The introduction contains relevant background information on the topic. However, it's slightly unclear due to the usage of technical terms without providing a clear definition or explanation.

1. Improvement: Make sure to clearly define all the terms and abbreviations you are using. Also, provide a clear research question or objective to give the reader an idea of what you aim to achieve with your study.

Method

There seems to be missing detailed explanation about the methods used to conduct the study. The methods section should provide enough details for someone else to reproduce your study.

2. Improvement: If applicable, describe the design of the study, the population studied, the treatments or interventions, and the type of statistical analysis. For example, how did you perform your network meta-analyses? What were your inclusion and exclusion criteria?

Results

The results are shared but might be unclear for people outside the field. For instance, the term 'renal composite outcome' is used but not defined.

3. Improvement: Be clear and concise about the results. Use common language to explain technical terms where possible. Make sure to provide context and definitions for terms like 'renal composite outcome'.

Discussion

The discussion has relevant insights but jumps between different studies and results quite abruptly.

4. Improvement: Provide more coherent and fluid connections between different points. Compare your results with previous studies in a structured way, emphasizing the novel contributions of your work. Also, the implications of your findings should be spelled out more clearly for the readers.

5. In all sections, remember to provide brief explanations of cited studies, such as the key findings or methodologies of the referenced works. Moreover, do edit for grammar and style to ensure the clarity and readability of the text.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2023 Nov 2;18(11):e0293183. doi: 10.1371/journal.pone.0293183.r006

Author response to Decision Letter 2

26 Jul 2023

Dear editor,

We thank the reviewers again for their efforts.

We have addressed the additional comments of Reviewer #2 and believe the manuscript is now appropriate for publication in PLOS ONE.

The introduction contains relevant background information on the topic. However, it's slightly unclear due to the usage of technical terms without providing a clear definition or explanation.

• Thank you for pointing this out. We added some explanations to several scientific terms (e.g. RAAS).

RAAS is a hormonal system that regulates the fluid and electrolyte balance and the systemic vascular system in the human body through various pathways, that affect the kidney. (Page 5, paragraph 2)

…. aldosterone, which is a steroid hormone and assumed to play a pivotal role in the development of chronic kidney disease. (Page 6, paragraph 3)

• In addition, we rephrased our objective to emphasize our goals and further elaborated our research question. (Page 6, paragraph 4)

Our objective is to identify the optimal treatment strategy in addition to RAAS blockade by ACEi/ARB for patients with DKD. We mainly focused on the following questions:

• Which drug most effectively reduces the risk of our primary endpoint?

• Which drug has the lowest risk of causing the side effects we have specified?

Method

There seems to be missing detailed explanation about the methods used to conduct the study. The methods section should provide enough details for someone else to reproduce your study.

• Thanks for the advice. We explained the technique of network meta-analysis in more detail.

Network meta-analysis is a statistical method for directly or indirectly comparing multiple interventions. It allows us to calculate the comparative effectiveness of interventions that have not been studied in head-to-head trials or have not been adequately studied. For each intervention, we applied the random effect model to generate the study effect sizes. (Page 9)

Data analysis was performed with the statistic program R version 4.0.4 using the “netmeta” package. (Page 9)

• We also added some references in the manuscript to our R code and the corresponding datasheet that we used to conduct the network meta-analysis.

The specific code and data sheet for our network meta-analysis can be found in the supporting information (S14, S15). (Page 9)

• We also defined eGFR and GRADE to make the manuscript more accessible to people outside the field.

eGFR (estimated Glomerular Filtration Rate) is a measure for the estimated kidney excretory function. (Page 9)

Moreover, we rated the quality of evidence of each pairwise comparison according to GRADE (Grading of Recommendations Assessment, Development and Evaluation) (Page 11)

Results

The results are shared but might be unclear for people outside the field. For instance, the term 'renal composite outcome' is used but not defined.

• We reevaluated our results section and implemented several changes:

Figure 2 shows the network structure for the primary outcome overall mortality, highlighting the larger number of clinical trials for SGLT2i, nsMRAs and ACEi + ARB. (Page 12)

eGFR (estimated Glomerular Filtration Rate) is a measure for the estimated kidney excretory function. (Page 9)

• The term “renal composite outcome” was defined in our results.

Discussion

The discussion has relevant insights but jumps between different studies and results quite abruptly.

• Thank you for the advice. We have made several changes:

However, steroidal MRAs, i.e. spironolactone and eplerenone, were disproportionally underrepresented for sufficient data on hard endpoints in our analysis.

A further evolution of MRA, nsMRAs are the most recently developed pharmacological agents acting on the RAAS system.

Unlike ACEi+ARB combination or MRAs, nsMRA did not cause higher rates of acute kidney injury (compared to single ACEi/ARB).

• The whole manuscript was proofread by a native speaker again.

Risk of bias was assessed using the RoB 2 tool (The Cochrane Collaboration’s tool for assessing risk of bias) and additionally reporting bias through comparison adjusted funnel plots which was applied to compare older treatments with newer treatments.

In our analysis, they were associated with a lower incidence of the renal composite outcome (compared to single ACEi/ARB).

Attachment

Submitted filename: Rebuttal letter 23.7.23.docx

Click here for additional data file.^{(19.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0293183.r007

Decision Letter 3

Licy Yanes Cardozo

21 Aug 2023

PONE-D-23-02576R3Treatment of diabetic kidney disease. A network meta-analysisPLOS ONE

Dear Dr. Büttner,

Please submit your revised manuscript by Oct 05 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Licy Yanes Cardozo

Academic Editor

PLOS ONE

Additional Editor Comments:

Edit for grammar and style to ensure the clarity and readability of the text

Abstract Please provide info re; methodology

Control groups: where is this patient on ACE//ARB only? Please clarify. What do you mean by placebo?

Hypothesis could be restated to better reflex the study

Citation for statistic program needed.

Please remove figure legends from results sections, hard to read

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: Areas for Enhancement:

1. Figure 1: Adopt a formal PRISMA flow diagram for clarity.

2. Introduction: Ensure technical terms and abbreviations are well-defined to enhance clarity for all readers.

3. Objective: Clearly articulate the study's purpose to guide readers.

4. Methods: Offer a more detailed account of the techniques employed to facilitate study replication.

5. Network Meta-analysis: Elaborate on the statistical methods and tools utilized for a comprehensive understanding.

6. R Code References: Incorporate references to the R code and associated datasheets to bolster transparency.

7. eGFR Definition: Clearly define eGFR (estimated Glomerular Filtration Rate) for broader audience comprehension.

8. GRADE Rating: Utilize the GRADE system to assess the quality of evidence for each comparison.

Potential Concerns:

1. Terminology: Ambiguous or undefined technical terms can confuse readers.

2. Methods Detailing: An insufficiently detailed methods section can compromise study reproducibility.

3. Analysis Transparency: Omitting specifics about analytical tools and methods can cast doubts on the study's credibility.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2023 Nov 2;18(11):e0293183. doi: 10.1371/journal.pone.0293183.r008

Author response to Decision Letter 3

19 Sep 2023

Dear editor,

We thank the reviewers again for their efforts.

We have addressed the additional comments of Reviewer #2 and believe the manuscript is now appropriate for publication in PLOS ONE.

Reviewer #2: Areas for Enhancement:

1. Figure 1: Adopt a formal PRISMA flow diagram for clarity.

• Thank you for pointing this out. We adopted the PRISMA flow diagram to the formal requirements.

2. Introduction: Ensure technical terms and abbreviations are well-defined to enhance clarity for all readers.

• We did make several adjustments.

o “Diabetes mellitus (DM) ranks among the top ten most devastating diseases worldwide, ….” (Page 5/ Paragraph 1)

o Single RAAS blockade was replaced by “Drugs based on a single RAAS Blockade in particular angiotensin-converting enzyme inhibitor (ACEi) or an angiotensin receptor blocker (ARB), were the only pharmacological approach to show considerable advantages” (Page 5/ Paragraph 2)

o Rephrased a sentence: “The evidence for the benefit of an additional RAAS blockade agent, either by combining ACEi+ ARB or one of them with e.g. mineralocorticoid receptor antagonists (MRA) or a direct renin inhibitor (DRI), remained limited.“ (Page 5/ Paragraph 2)

o Acute kidney injury was defined. “(abrupt changes in kidney function, including serum creatinine changes and urine output within 48 hours or 7 days) KDIGO” (Page 5/ Paragraph 2)

3. Objective: Clearly articulate the study's purpose to guide readers.

• We rephrased the study´s purpose (Page 6):

o Our objective is to identify the optimal treatment strategy in addition to ACEi or ARB for patients with DKD. We mainly focused on the following questions:

� Which drug most effectively reduces the risk of overall mortality, the development of end stage kidney disease, acute kidney injury, hyperkalemia and hypotension?

� Which medication carries the least risk of inducing the specific side effects we have outlined?

4. Methods: Offer a more detailed account of the techniques employed to facilitate study replication.

• We added the following information:

The following characteristics were extracted study name, registration number, background therapy, type of study, inclusion criteria, exclusion criteria, study duration, study drug, dosage, mean age, percentage of male patients, outcome data and history of hypertension. (Page 8/ Paragraph 2)

• We did incorporate several new explanations to specific R-packages and its functions.

o We computed the network plot with the “netgraph” function from the “netmeta” package, the comparison-adjusted funnel plots with the function “funnel” and the splitting of direct and indirect evidence with the “netsplit” function. (page 10 paragraph 2)

o The I2 value were computed with the “netmeta” function. (page 10/ paragraph 3)

• Additional information has been added.

o “Risk of bias was assessed by two authors using the RoB 2 tool.” (page 10/paragraph 1)

• The search strategy and the corresponding results may be found in the supporting information document under S4.

5. Network Meta-analysis: Elaborate on the statistical methods and tools utilized for a comprehensive understanding.

• We further elaborated on the terms: design inconsistency, heterogeneity, net heat plot and loop inconsistency.

• Net heat plot

o The gray squares represent the degree of importance of one treatment comparison for the estimation of another treatment comparison and the color in the background illustrates the degree of inconsistency. (page 10, paragraph 3)

• design inconsistency & loop inconsistency

o Design inconsistency stands for discrepancies in effect estimates between studies involving different composition of interventions. Loop inconsistency evaluates if direct and indirect evidence correspond with each other (9). (page 10, paragraph 3)

• Heterogeneity

o Heterogeneity refers to the variability or differences in effect estimates across studies within a network (9). (page 10, paragraph 2)

6. R Code References: Incorporate references to the R code and associated datasheets to bolster transparency.

• We did incorporate several new explanations to specific R-packages and its functions.

o The I2 value were computed with the “netmeta” function. (page 10/ paragraph 3)

• The specific R-code and data sheet for our network meta-analysis can be found in the supporting information (S14, S15).

7. eGFR Definition: Clearly define eGFR (estimated Glomerular Filtration Rate) for broader audience comprehension.

• eGFR (Estimated Glomerular Filtration Rate) is a clinical measurement used in nephrology to estimate the rate at which the glomeruli are filtering waste products and excess substances from the blood per unit of time. (Page 9)

8. GRADE Rating: Utilize the GRADE system to assess the quality of evidence for each comparison.

• We have included the data related to the quality of evidence (GRADE system) for hypotension and the composite outcome in the supporting information S16. The GRADE assessment for the remaining interventions can be found in tables T1 and T2, making the GRADE assessment accessible for all comparative analysis.

Potential Concerns:

1. Terminology: Ambiguous or undefined technical terms can confuse readers.

• We reevaluated the manuscript for ambiguous or undefined terms. (e.g. We switched in the introduction “single RAAS blockade” for “drugs based on a single RAAS blockade” to improve the understanding, added a definition for acute kidney injury)

• If there are other terms that are not defined, feel free to point them out.

2. Methods Detailing: An insufficiently detailed methods section can compromise study reproducibility.

• We elaborated further on details concerning the methods. (e.g. additional explanation of R -packages and which are used, several other details were added)

3. Analysis Transparency: Omitting specifics about analytical tools and methods can cast doubts on the study's credibility.

• We have implemented concerns/comments from the “area for improvement” and added more details to the methods section. Our whole R-code and the corresponding data may be found in the supporting information file under S4.

Additional Editor Comments:

Edit for grammar and style to ensure the clarity and readability of the text

• Noted. We did implement several changes related to grammar and style.

Abstract Please provide info re; methodology

• Risk of bias, GRADE approach were added.

Control groups: where is this patient on ACE//ARB only? Please clarify. What do you mean by placebo?

• In most clinical trials we assessed, the intervention of interest (e.g., SGLT2i) was typically compared to either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker, and in some cases, a placebo might have been included as well. We referred to this combination as an active control. No clinical trials solely employed a placebo. However, the most common combinations involved ACEi/ARB with either a placebo or ACEi/ARB alone. Our control group consists of mostly an active control as ACEi or ARB drug combination.

• We did not specify placebo any further.

Hypothesis could be restated to better reflex the study

• The hypothesis underwent a revision (Page 5).

Citation for statistic program needed.

• Citation was added. (Page 9)

Please remove figure legends from results sections, hard to read

• Implemented. The corresponding figure legends may be found at the end of the document. (Page 26)

Attachment

Submitted filename: rebuttal letter.docx

Click here for additional data file.^{(30.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0293183.r009

Decision Letter 4

Licy Yanes Cardozo

9 Oct 2023

Treatment of diabetic kidney disease. A network meta-analysis

PONE-D-23-02576R4

Dear Dr. Büttner,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Licy Yanes Cardozo

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All comments were addressed by the authors

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: nothing to say more than authors addressed all comments-and this is NOT needed so should be dropped as it is a

Reviewer #2: It appears that all comments have been appropriately responded to. I have no further comments and recommend publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0293183.r010

Acceptance letter

Licy Yanes Cardozo

24 Oct 2023

PONE-D-23-02576R4

Treatment of diabetic kidney disease. A network meta-analysis

Dear Dr. Büttner:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Licy Yanes Cardozo

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Baseline characteristics.

(PDF)

Click here for additional data file.^{(228.8KB, pdf)}

S2 File. Citation list.

(PDF)

Click here for additional data file.^{(89.2KB, pdf)}

S3 File. Prisma statements 2020.

(PDF)

Click here for additional data file.^{(177KB, pdf)}

S4 File. Search strategy.

(PDF)

Click here for additional data file.^{(30.7KB, pdf)}

S5 File. Tests of heterogeneity (within designs) and inconsistency (between designs) with Cochran’s Q statistics.

(PDF)

Click here for additional data file.^{(83.2KB, pdf)}

S6 File. Distribution of direct and indirect evidence.

(PDF)

Click here for additional data file.^{(769.8KB, pdf)}

S7 File. GRADE protocol.

(PDF)

Click here for additional data file.^{(106.2KB, pdf)}

S8 File. Splitting into direct and indirect evidence.

(PDF)

Click here for additional data file.^{(1MB, pdf)}

S9 File. Risk of bias (given in excel).

(PDF)

Click here for additional data file.^{(320.8KB, pdf)}

S10 File. Sensitivity analysis (RoB).

(PDF)

Click here for additional data file.^{(30.2KB, pdf)}

S11 File. Sensitivity analysis (single ACEi/ARB mandatory in control groups).

(PDF)

Click here for additional data file.^{(29.7KB, pdf)}

S12 File. Sensitivity analysis (replacing FIGARO and FIDELIO bei FIDELITY).

(PDF)

Click here for additional data file.^{(86.6KB, pdf)}

S13 File. Albuminuria, renal composite outcome (sustained eGFR <15 mL/ min/1.73 m2, sustained eGFR decline of 40% from baseline or kidney death).

(PDF)

Click here for additional data file.^{(79.8KB, pdf)}

S14 File. Renal composite outcome (sustained eGFR <15 mL/ min/1.73 m2, sustained eGFR decline of 40% from baseline or kidney death).

(PDF)

Click here for additional data file.^{(62.3KB, pdf)}

S15 File. R-code.

(PDF)

Click here for additional data file.^{(36.6KB, pdf)}

S16 File. Data sheets.

(PDF)

Click here for additional data file.^{(202.1KB, pdf)}

S17 File. GRADE for renal composite outcome.

(PDF)

Click here for additional data file.^{(72.6KB, pdf)}

S18 File. GRADE for hypotension.

(PDF)

Click here for additional data file.^{(77.8KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(37.8KB, docx)}

Attachment

Submitted filename: rebuttal letter_2_BS-2.docx

Click here for additional data file.^{(76.6KB, docx)}

Attachment

Submitted filename: Rebuttal letter 23.7.23.docx

Click here for additional data file.^{(19.9KB, docx)}

Attachment

Submitted filename: rebuttal letter.docx

Click here for additional data file.^{(30.8KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting information files.

[pone.0293183.ref001] 1.Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020;396(10258):1204–22. doi: 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref002] 2.Li H, Lu W, Wang A, Jiang H, Lyu J. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: Estimates from Global Burden of Disease 2017. J Diabetes Investig. 2021;12(3):346–56. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref003] 3.Palmer SC, Mavridis D, Navarese E, Craig JC, Tonelli M, Salanti G, et al. Comparative efficacy and safety of blood pressure-lowering agents in adults with diabetes and kidney disease: A network meta-analysis. The Lancet. 2015;385(9982):2047–56. doi: 10.1016/S0140-6736(14)62459-4 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref004] 4.Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Vol. 120, Nephron: —Clinical Practice. 2012. [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref005] 5.Fried LF, Emanuele N, Zhang JH, Brophy M, Conner TA, Duckworth W, et al. Combined angiotensin inhibition for the treatment of diabetic nephropathy. N Engl J Med. 2013. Nov;369(20):1892–903. doi: 10.1056/NEJMoa1303154 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref006] 6.Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJLL, Charytan DM, et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. New England journal of medicine. 2019. Jun;380(24):2295‐2306. doi: 10.1056/NEJMoa1811744 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref007] 7.Harrer M, Cuijpers P, Furukawa TA, Ebert DD. Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL and London: Chapman & Hall/CRC Press; 2021. ISBN 978-0-367-61007-4. [Google Scholar]

[pone.0293183.ref008] 8.Salanti G, Del Giovane C, Chaimani A, Caldwell DM, Higgins JPT. Evaluating the Quality of Evidence from a Network Meta- Analysis. 2014;9(7). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref009] 9.Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ W V (editors). Cochrane Handbook for Systematic Reviews of Interventions. 2nd edition. Chichester (UK): John Wiley & Sons; 2019. [Google Scholar]

[pone.0293183.ref010] 10.Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta‐analysis: concepts and models for multi‐arm studies. Res Synth Methods. 2012. Jun;3(2):98–110. doi: 10.1002/jrsm.1044 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref011] 11.Krahn U, Binder H, König J. A graphical tool for locating inconsistency in network meta-analyses. BMC Med Res Methodol. 2013;13(1). doi: 10.1186/1471-2288-13-35 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref012] 12.Puhan MA, Schünemann HJ, Murad MH, Li T, Brignardello-Petersen R, Singh JA, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ (Online). 2014;349(September):1–10. [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref013] 13.Brignardello-Petersen R, Bonner A, Alexander PE, Siemieniuk RA, Furukawa TA, Rochwerg B, et al. Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis. J Clin Epidemiol. 2018;93:36–44. [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref014] 14.DeFronzo RA, Reeves WB, Awad AS. Pathophysiology of diabetic kidney disease: impact of SGLT2 inhibitors. Nat Rev Nephrol. 2021;17(5):319–34. doi: 10.1038/s41581-021-00393-8 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref015] 15.Wanner C, Inzucchi SE, Lachin JM, Fitchett D, von Eynatten M, Mattheus M, et al. Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes. New England Journal of Medicine. 2016;375(4):323–34. doi: 10.1056/NEJMoa1515920 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref016] 16.McMurray JJV, Wheeler DC, Stefánsson B V., Jongs N, Postmus D, Correa-Rotter R, et al. Effects of Dapagliflozin in Patients With Kidney Disease, With and Without Heart Failure. JACC Heart Fail. 2021;9(11):807–20. [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref017] 17.Layton AT, Vallon V, Edwards A. Predicted consequences of diabetes and SGLT inhibition on transport and oxygen consumption along a rat nephron. Am J Physiol Renal Physiol. 2016;310(11):F1269–83. doi: 10.1152/ajprenal.00543.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref018] 18.Thomson SC, Rieg T, Miracle C, Mansoury H, Whaley J, Vallon V, et al. Acute and chronic effects of SGLT2 blockade on glomerular and tubular function in the early diabetic rat. Am J Physiol Regul Integr Comp Physiol. 2012;302(1):75–83. doi: 10.1152/ajpregu.00357.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref019] 19.DeFronzo RA, Norton L, Abdul-Ghani M. Renal, metabolic and cardiovascular considerations of SGLT2 inhibition. Nat Rev Nephrol. 2017;13(1):11–26. doi: 10.1038/nrneph.2016.170 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref020] 20.Han JH, Oh TJ, Lee G, Maeng HJ, Lee DH, Kim KM, et al. The beneficial effects of empagliflozin, an SGLT2 inhibitor, on atherosclerosis in ApoE −/− mice fed a western diet. Diabetologia. 2017;60(2):364–76. doi: 10.1007/s00125-016-4158-2 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref021] 21.Vallon V, Gerasimova M, Rose MA, Masuda T, Satriano J, Mayoux E, et al. SGLT2 inhibitor empagliflozin reduces renal growth and albuminuria in proportion to hyperglycemia and prevents glomerular hyperfiltration in diabetic Akita mice. Am J Physiol Renal Physiol. 2014;306(2):194–204. doi: 10.1152/ajprenal.00520.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref022] 22.Ni L, Yuan C, Chen G, Zhang C, Wu X. SGLT2i: beyond the glucose—lowering effect. Cardiovasc Diabetol. 2020;1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref023] 23.Menne J, Dumann E, Haller H, Schmidt BMW. Acute kidney injury and adverse renal events in patients receiving SGLT2-inhibitors: A systematic review and meta-analysis. PLoS Med. 2019;16(12):1–13. doi: 10.1371/journal.pmed.1002983 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref024] 24.Schmieder RE, Hilgers KF, Schlaich MP, Schmidt BM. Renin-angiotensin system and cardiovascular risk. Lancet. 2007;369(9568):1208–19. doi: 10.1016/S0140-6736(07)60242-6 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref025] 25.van den Meiracker AH, Baggen RGAG, Pauli S, Lindemans A, Vulto AG, Poldermans D, et al. Spironolactone in type 2 diabetic nephropathy: Effects on proteinuria, blood pressure and renal function. J Hypertens. 2006. Nov;24(11):2285–92. doi: 10.1097/01.hjh.0000249708.44016.5c [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref026] 26.Parving HH, Brenner BM, McMurray JJ V, de Zeeuw D, Haffner SM, Solomon SD, et al. Cardiorenal end points in a trial of aliskiren for type 2 diabetes. N Engl J Med. 2012. Dec;367(23):2204–13. doi: 10.1056/NEJMoa1208799 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref027] 27.Grune J, Beyhoff N, Smeir E, Chudek R, Blumrich A, Ban Z, et al. Selective mineralocorticoid receptor cofactor modulation as molecular basis for finerenone’s antifbrotic activity. Hypertension. 2018;71(4):599–608. [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref028] 28.Kolkhof P, Delbeck M, Kretschmer A, Steinke W, Hartmann E, Bärfacker L, et al. Finerenone, a novel selective nonsteroidal mineralocorticoid receptor antagonist protects from rat cardiorenal injury. J Cardiovasc Pharmacol. 2014. Jul;64(1):69–78. doi: 10.1097/FJC.0000000000000091 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref029] 29.Bakris GL, Filippatos G, Bakris GL, Agarwal R, Anker SD, Pitt B, et al. Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. New England journal of medicine. 2020;383(23):2219‐2229. doi: 10.1056/NEJMoa2025845 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref030] 30.Pitt B, Filippatos G, Agarwal R, Anker SD, Bakris GL, Rossing P, et al. Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. New England Journal of Medicine. 2021. Aug 28;385(24):2252–63. doi: 10.1056/NEJMoa2110956 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref031] 31.Rossing P, Caramori ML, Chan JCN, Heerspink HJL, Hurst C, Khunti K, et al. Executive summary of the KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease: an update based on rapidly emerging new evidence. Kidney Int. 2022;102(5):990–9. doi: 10.1016/j.kint.2022.06.013 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref032] 32.de Boer IH, Khunti K, Sadusky T, Tuttle KR, Neumiller JJ, Rhee CM, et al. Diabetes management in chronic kidney disease: a consensus report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Vol. 102, Kidney international. United States; 2022. p. 974–89. [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref033] 33.Rossing P, Caramori ML, Chan JCN, Heerspink HJL, Hurst C, Khunti K, et al. Executive summary of the KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease: an update based on rapidly emerging new evidence. Vol. 102, Kidney international. United States; 2022. p. 990–9. [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref034] 34.Rossing P, Filippatos G, Agarwal R, Anker SD, Pitt B, Ruilope LM, et al. Finerenone in Predominantly Advanced CKD and Type 2 Diabetes With or Without Sodium-Glucose Cotransporter-2 Inhibitor Therapy. Kidney Int Rep. 2022;7(1):36–45. doi: 10.1016/j.ekir.2021.10.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref035] 35.Goulooze SC, Heerspink HJL, van Noort M, Snelder N, Brinker M, Lippert J, et al. Dose–Exposure–Response Analysis of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone on UACR and eGFR: An Analysis from FIDELIO-DKD. Clin Pharmacokinet. 2022;61(7):1013–25. doi: 10.1007/s40262-022-01124-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref036] 36.Green JB, Mottl AK, Bakris G, Heerspink HJL, Mann JFE, McGill JB, et al. Design of the COmbinatioN effect of FInerenone anD EmpaglifloziN in participants with chronic kidney disease and type 2 diabetes using an UACR Endpoint study (CONFIDENCE). Nephrology Dialysis Transplantation. 2022;(June):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref037] 37.De Zeeuw D, Remuzzi G, Parving HH, Keane WF, Zhang Z, Shahinfar S, et al. Proteinuria, a target for renoprotection in patients with type 2 diabetic nephropathy: Lessons from RENAAL. Kidney Int. 2004;65(6):2309–20. doi: 10.1111/j.1523-1755.2004.00653.x [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref038] 38.Waijer SW, Gansevoort RT, Heerspink HJL. Change in albuminuria as a surrogate endpoint. Curr Opin Nephrol Hypertens. 2019;28(6):519–26. doi: 10.1097/MNH.0000000000000541 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref039] 39.Bakris GL, Filippatos G, Agarwal R, Anker SD, Pitt B, et al. Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. New England journal of medicine. 2020;383(23):2219‐2229. doi: 10.1056/NEJMoa2025845 [DOI] [PubMed] [Google Scholar]

[pone.0293183.ref040] 40.Cao H, Liu T, Wang L, Ji Q. Comparative efficacy of novel antidiabetic drugs on cardiovascular and renal outcomes in patients with diabetic kidney disease: A systematic review and network meta-analysis. Diabetes Obes Metab. 2022;24(8):1448–57. doi: 10.1111/dom.14702 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0293183.ref041] 41.Ghosal S, Sinha B. Assessing the Effects of Modern Renoprotective Agents in Preventing Progression of Renal Composite Outcomes in Patients with Type 2 Diabetes: A Network Meta-analysis. Diabetes Therapy. 2022. doi: 10.1007/s13300-022-01359-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Treatment of diabetic kidney disease. A network meta-analysis

Fabian Büttner

Clara Vollmer Barbosa

Hannah Lang

Zhejia Tian

Anette Melk

Bernhard M W Schmidt

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Fig 1. Flow chart for selection process.

Outcomes

Data analysis

Role of the funding source

Results

Mortality

Fig 2. Network plot for overall mortality and hyperkalemia.

Fig 3. Forest plot for overall mortality.

Table 1. Overall mortality and end stage kidney disease.

ESKD

Fig 4. Forest plot for end stage kidney disease.

Secondary endpoints

Safety endpoints

Fig 5. Forrest plot for the following safety outcomes: Acute kidney injury, hyperkalemia, hypotension.

Table 2. Hyperkalemia and acute kidney injury.

Sensitivity analyses

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Licy Yanes Cardozo

Roles

Author response to Decision Letter 0

Decision Letter 1

Licy Yanes Cardozo

Roles

Author response to Decision Letter 1

Decision Letter 2

Licy Yanes Cardozo

Roles

Author response to Decision Letter 2

Decision Letter 3

Licy Yanes Cardozo

Roles

Author response to Decision Letter 3

Decision Letter 4

Licy Yanes Cardozo

Roles

Acceptance letter

Licy Yanes Cardozo

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases